The objective of this project is to build a Business Intelligence system that analyzes student academic data to provide personalized learning recommendations. The platform will use data analytics and visualization techniques to monitor performance trends, identify at-risk students, and support data-driven educational decision-making.
Collect or simulate student performance datasets including attendance, grades, and assessment records.
Clean and preprocess the dataset using data transformation techniques.
Design a data warehouse structure optimized for academic analytics.
Load structured data into a BI tool such as Power BI or Tableau.
Create dashboards displaying student performance metrics, attendance patterns, and subject-wise analytics.
Implement classification algorithms (e.g., decision trees or logistic regression) to predict at-risk students.
Integrate predictive results into the dashboard for visualization.
Develop recommendation logic for personalized learning interventions.
Enable role-based access for teachers and administrators.
Validate model accuracy using evaluation metrics such as confusion matrix or accuracy score.
Document the complete BI workflow including ETL processes and analytics implementation.
